Introduction to GPT-RAG
GPT-RAG, or the Retrieval-Augmented Generation pattern, is an innovative approach that combines data retrieval with large language models (LLMs) to enhance business operations with AI-powered responses. This project utilizes Azure's powerful AI Search for efficient data retrieval and Azure OpenAI models to deliver ChatGPT-like conversational experiences.
Aimed at enterprises, the GPT-RAG Solution Accelerator offers a secure, scalable, and auditable architecture underpinned by Zero-trust security and Responsible AI principles. It is especially beneficial for organizations using AI solutions in full-scale production, providing a path from exploration and proofs of concept to robust and reliable AI deployments.
Key Components
GPT-RAG is designed with a modular architecture featuring three main components, each serving a specific purpose:
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Data Ingestion: This component, accessible via a GitHub repository, ensures efficient chunking and indexing of data, streamlining the retrieval process needed for generating informed responses.
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Orchestrator: Manages the flow of retrieving data and delivering responses, available in two versions. The Functional option uses Semantic Kernel functions, while the Agentic version utilizes AutoGen agents. Users can follow specific deployment instructions to choose the Agentic option.
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App Front-End: Employs the Backend for Front-End pattern, presenting a web interface that balances performance and scalability.
Deployment Options
GPT-RAG offers flexible deployment strategies to fit different security and network requirements:
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Basic Architecture Deployment: Ideal for demonstrations or initial testing phases, this option requires minimal network isolation and can be set up quickly using the Azure Developer CLI.
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Standard Zero-Trust Architecture Deployment: Recommended for environments where secure, Zero-trust network postures are essential. This deployment comes with pre-configured settings, demanding no further customization.
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Custom Zero-Trust Architecture Setup: For tailored security setups, this popular option allows users to adjust the architecture and solution settings according to their organizational needs and policies.
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Step-by-Step Manual Setup: A detailed, hands-on guide for those who prefer more control over the deployment process, emphasizing Zero-trust architecture.
Using GPT-RAG
To get started, users should ensure they have all necessary tools and services, detailed in the pre-requisites list, including Azure Developer CLI, Powershell, Git, Node.js, and Python. Following the set-up guides, users can deploy either the Basic or Zero Trust architectures, accommodating various operational needs from proof-of-concept to secure production environments.
Users are encouraged to personalize their deployment and explore integrating additional data sources, such as Bing Custom Search or SQL server, to expand their GPT-RAG solutions further. A variety of guides and resources are available to assist with customization, troubleshooting, performance evaluation, and cost estimation.
Community and Contribution
The Enterprise RAG community offers collaborative platforms, like a Discord Channel, enabling users and developers to share insights and seek support. Furthermore, users are invited to contribute to the project, adhering to the guidelines spelled out in the contributing documents, with openness to enhancements and new ideas.
By fostering a community of innovators and providing robust tools for its deployment and operation, GPT-RAG stands as a comprehensive solution for businesses looking to harness the power of AI-driven interactions in a secure and scalable manner.